This invention relates generally to large scale data processing systems and more particularly, to the architecture of large single instruction multiple data (SIMD) type parallel processing arrays for scientific processing applications.
In the development of digital computers the most important design goal has always been to maximize their operating speed, i.e., the amount of data that can be processed in a unit of time. It has become increasingly apparent in recent times that two important limiting conditions exist within the present framework of computer design. These are the limits of component speed and of serial machine organization. To overstep these limitations high speed parallel processing systems have been developed providing an array of processing elements under the control of a single control unit.
As speed requirements of computation have continued to increase, systems employing greater numbers of parallel memory modules have been developed. One such system has in the order of 64 parallel memories, see U.S. Pat. No. 3,537,074, issued Oct. 27, 1970 to R. A. Stokes et al, and assigned to the assignee of the present invention. However, parallel processors have not been without their own problems. For example, a parallel array often has great capacity that is unusable because of limitations imposed by the I/O channels feeding data to it. Further, the parallel array being tailored to vector or parallel processing performs relatively slowly while handling scalar tasks.
Also, parallel processors being architecturally so far removed from scalar processors often are hard to program and have limited ability to function with standard high level languages such as Fortran.
Finally, prior art parallel processors often have difficulty handling matrix calculations which are often the heart of scientific problems. Unless each element of a matrix vector is stored in a different memory module in the array memory that vector cannot be accessed in parallel and a memory conflict occurs slowing and complicating matrix calculations.